DineTogether: A Social-Aware Group Sequential Recommender System

Group recommendations become important in many practical scenarios, e.g., couples would like to share movies together, families dine together in a restaurant, a group of friends plan to spend vacation in some points of interest. Although some previous research efforts have been done in this area, most of them only consider a small portion of contextual factors but ignore the time series features of the user historical behavior and next recommended location/event to do, that makes the system performance not as satisfactory as expected. Here, we propose a novel group sequential recommender system, called “DineTogether”, for a group of people dinning together. It is able to capture comprehensive contextual, social factors when make recommendations.We design a computational model, called “Social-and-Time-Aware (STA)” model, and a novel algorithm, Generalized Random Walk with Restart (GRWR). Experiment results show that our approach outperforms the state-of-the-art group recommendation approaches.